GraphAcademy

Using Neo4j with LangChain

Neo4j and LangChain

Vectors

Text to Cypher

Neo4jGraph

You can query Neo4j from a LangChain application using the Neo4jGraph class. The Neo4jGraph class acts as the connection to the database when using other LangChain components, such as retrievers and agents.

In this lesson, you will modify the simple LangChain agent to be able to answer questions about a graph database schema.

The database contains information about movies, actors, and user ratings.

Query Neo4j

To query Neo4j you need to:

  1. Create a Neo4jGraph instance and connect to a database

  2. Run a Cypher statement to get data from the database

Open the genai-integration-langchain/neo4j_query.py file:

python
neo4j_query.py
Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/neo4j_query.py[tag=**]

Update the code to:

  1. Connect to the Neo4j database using your connection details:

    python
    Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/solutions/neo4j_query.py[tag=import_neo4j]

Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/solutions/neo4j_query.py[tag=neo4jgraph]

  2. Run a Cypher query to retrieve data from the database:

    python
    Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/solutions/neo4j_query.py[tag=query]

The results are returned as a list of dictionaries, where each dictionary represents a row in the results.

[
    {'actor': 'Henry Czerny', 'role': 'Eugene Kittridge'},
    {'actor': 'Emmanuelle Béart', 'role': 'Claire Phelps'},
    {'actor': 'Tom Cruise', 'role': 'Ethan Hunt'},
    {'actor': 'Jon Voight', 'role': 'Jim Phelps'}
]

You can use data returned from queries as context for an agent.

Schema

You are going to modify the agent to retrieve the database schema and add it to the context.

You can view the database schema using the Cypher query:

cypher
CALL db.schema.visualization()

Open the genai-integration-langchain/scheme_agent.py file.

python
schema_agent.py
Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/schema_agent.py[tag=**]

Add the code to create a connection to the Neo4j database using the Neo4jGraph class:

python
Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/solutions/schema_agent.py[tag=import_neo4jgraph]

Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/solutions/schema_agent.py[tag=neo4jgraph]

Modify the retrieve function to use the Neo4jGraph instance to query the database and retrieve the schema information:

python
Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/solutions/schema_agent.py[tag=retrieve]

The agent will add the database schema to the context and use it to answer questions about the database.

Update the question and run the application:

python
Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/solutions/schema_agent.py[tag=question]
Click to see the complete code
python
Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/solutions/schema_agent.py[tags="**;!examples"]

When asked "How is the graph structured?", the agent will response with a summary of the graph, typically including details about nodes, relationships, and their properties.

Click to see an example response

The graph is structured using nodes and relationships.

  • Nodes:

    • Movie: Has indexes on year, imdbRating, released, imdbId, title, and tagline. It has unique constraints on movieId and tmdbId.

    • User: Has an index on name and a unique constraint on userId.

    • Actor, Director, and Genre: Do not have indexes listed. The Genre node has a unique constraint on name.

    • Person: Has an index on name and a unique constraint on tmdbId.

  • Relationships:

    • ACTED_IN: Connects Actor, Director, or Person nodes with Movie nodes.

    • RATED: Connects User nodes to Movie nodes.

    • IN_GENRE: Connects Movie nodes to Genre nodes.

    • DIRECTED: Connects Person, Actor, or Director nodes to Movie nodes.

In summary, the graph represents a network where movies are connected to various entities like users, genres, actors, directors, and general persons through specific relationships indicating roles like acting, directing, rating, and categorization into genres.

Experiment by asking the agent other questions about the database schema. Here are some examples:

Unresolved directive in lesson.adoc - include::{repository-raw}/new-course/genai-integration-langchain/solutions/schema_agent.py[tag=examples]

When you are ready, continue to the next module.

Lesson Summary

In this lesson, you learned how to query Neo4j from LangChain and updated the agent to retrieve the database schema.

In the next module, you learn how to use vectors to create RAG and GraphRAG retrievers.